Beating the Odds & Benchmarking for Data Analytics

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Olympia London

Hammersmith Rd · London

How to find us

Self-Service Analytics Theatre

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Details

BENCHMARKING AND DATA ANALYTICS FOR IMPROVED PROJECT PERFORMANCE

Tim will introduce the subject of Benchmarking and illustrate with professional and personal examples. He will describe how considered use of data can drive continuous improvement, support target setting and
foster sharing and learning across an organisation and with outside peers; and will explain how benchmarking can be applied to improving Project Performance and illustrate with examples of cost, schedule and performance benchmarking in addition to benchmarking for project best practice.

Project Management topics that will be covered;
- Benchmarking
- Continuous Improvement
- Target Setting
- Knowledge Management
- Best Practices

The presentation will explore the potential for data analytics to enhance benchmarking by providing more insights around project plans, risks and practices and in real time show leading indicators of project progress and potential issues.

BIO: TIM PODESTA
Tim is a subject matter expert in project management; with particular interest in business/investment analysis, front end planning, benchmarking and assurance. He has deep experience of the oil, gas and petrochemicals industry. Tim has a track record of delivering cross cultural programmes in change management and process improvement. He celebrated 35 years with BP in 2016. In his last role with BP Tim led the back office for a major global corporate programme in the matter of safe and reliable operations which made significant improvements to operating performance across the group. Tim is an active member of PMI
and the UK Chapter; he has presented at PMI Synergy and other Local Events. He is a member of PMI Toastmasters which meets twice a month in London. Tim is an experienced facilitator of live events and can work in English and French.

BEATING THE ODDS: HOW TO MAKE YOUR DATA PROJECT OR TEAM PART OF THE 15% SUCCESS STORY

Making data science a success is really hard with up to 85% of projects and initiatives around big data and data science failing, according to Gartner. The reasons are complex but often misunderstood. As project management begins to grapple with the opportunities presented by data science those responsible for implementation need to proceed with care.

What is so different about data that it needs new approaches? This talk will focus on the requirements for data science success and looks at a future after the hype:

Motivation: Vanity project or aligned business strategy with senior leadership buy-in?
Requirements and preparations: Solid foundations or duct taped data silos and constant fire fighting bad data?
Hiring: Unicorns with the right skill sets to be a commercial data scientist or expensive mis-hires?
Delivery: Models in production serving business needs or undocumented proof of concepts on laptops?
Retention: Roadmap of game changing projects or abandoned team and expensive write-offs?

BIO: JAN TEICHMANN
Jan is a successful leader in the data transformation efforts of companies and has a track record of bringing data science into commercial production usage at scale. He previously co-founded Cambridge Energy Data Lab where they celebrated a successful exit with Enechange.jp, an utility comparison platform, which is now the market leader in Japan.

Jan is a highly skilled data scientist, data engineer and solution architect. He holds a PhD in Mathematics and offers a strong background in machine learning, statistical modelling and programming. He has extensive experience in big data, full stack development and interactive data visualisations which he uses to deliver engaging and comprehensive data science products which make an impact.

Please note, the revised date and venue for this meetup. We will be hosting our meetup at Olympia (in the Self-Service Analytics Theatre), London following Big Data London, the UK's largest data and analytics conference and exhibition. To attend please make sure you register at https://bigdataldn.com/register.